Lab #2 - Perceptron Classification [AMEO]¶
Author - Aman Hussain
Reg. - 15BCE1077
Email - aman.hussain2015@vit.ac.in
Import Libraries¶
In [1]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
from sklearn.preprocessing import normalize
Load the data¶
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graduate_data = pd.read_csv('../Data/processed/X.csv')
graduate_data.tail()
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In [4]:
salary_category = pd.read_csv('../Data/processed/y_cat.csv')
salary_category.head()
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Data Analysis¶
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data = graduate_data
data['Salary'] = salary_category['Salary']
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graduate_data.columns
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In [7]:
features = ['10percentage', '12percentage', 'collegeGPA', 'English',
'Logical','Quant', 'Domain', 'conscientiousness', 'agreeableness',
'extraversion', 'nueroticism', 'openess_to_experience','Specialization_CE',
'Specialization_CS', 'Specialization_EC', 'Specialization_EL',
'Specialization_ME', 'Specialization_other', 'CollegeTier_1',
'CollegeTier_2', 'Degree_B.Tech/B.E.','Degree_M.Sc. (Tech.)',
'Degree_M.Tech./M.E.', 'Degree_MCA']
In [8]:
sns.pairplot(data,
vars=['10percentage', '12percentage', 'collegeGPA', 'English','Logical','Quant', 'Domain'],
hue='Salary')
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In [9]:
sns.pairplot(data,
vars=['conscientiousness', 'agreeableness', 'extraversion', 'nueroticism', 'openess_to_experience'],
hue='Salary')
Out[9]:
In [10]:
sns.pairplot(data,
vars=['Specialization_CE', 'Specialization_CS', 'Specialization_EC',
'Specialization_EL', 'Specialization_ME', 'Specialization_other'],
hue='Salary')
Out[10]:
In [11]:
sns.pairplot(data,
vars=['CollegeTier_1', 'CollegeTier_2', 'Degree_B.Tech/B.E.',
'Degree_M.Sc. (Tech.)', 'Degree_M.Tech./M.E.', 'Degree_MCA'],
hue='Salary')
Out[11]:
In [12]:
sns.pairplot(data,
vars=['conscientiousness', 'agreeableness', 'extraversion', 'nueroticism', 'openess_to_experience',
'collegeGPA', 'English','Logical','Quant', 'Domain'],
hue='Salary')
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The data is not linearly separable, no matter how we look at it. Hence, we can be pretty sure that a single Perceptron classifier will do a bad job. However, for demonstration and visualization purposes we choose the two features: 'domain' & 'agreeableness'.
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salary_freq = salary_category['Salary'].value_counts()
salary_1 = salary_freq['(180000, 300000]']
total = salary_freq.sum()
print('Percentage of (180000, 300000]: {:.2f}'.format(salary_1 / total))
Feature Engineering¶
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del data
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X = graduate_data.as_matrix(columns=['Domain', 'agreeableness'])
y = salary_category.as_matrix()
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y1 = np.where(y == '(180000, 300000]', -1, 1).ravel()
y2 = np.where(y == '[35000, 180000]', -1, 1).ravel()
y3 = np.where(y == '(370000, 4000000]', -1, 1).ravel()
y4 = np.where(y == '(300000, 370000]', -1, 1).ravel()
y = [y1, y2, y3, y4]
Perceptron¶
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class Perceptron(object):
"""Perceptron classifier.
Parameters
------------
eta : float
Learning rate (between 0.0 and 1.0)
n_iter : int
Passes over the training dataset.
Attributes
-----------
w_ : 1d-array
Weights after fitting.
errors_ : list
Number of misclassifications in every epoch.
"""
def __init__(self, eta=0.01, n_iter=10):
self.eta = eta
self.n_iter = n_iter
def fit(self, X, y):
"""Fit training data.
Parameters
----------
X : {array-like}, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : object
"""
self.w_ = np.zeros(1 + X.shape[1])
self.errors_ = []
for _ in range(self.n_iter):
errors = 0
for xi, target in zip(X, y):
update = self.eta * (target - self.predict(xi))
self.w_[1:] += update * xi
self.w_[0] += update
errors += int(update != 0.0)
self.errors_.append(errors)
return self
def net_input(self, X):
"""Calculate net input"""
return np.dot(X, self.w_[1:]) + self.w_[0]
def activation(self, X):
"""Compute linear activation"""
return self.net_input(X)
def predict(self, X):
"""Return class label after unit step"""
return np.where(self.activation(X) >= 0.0, 1, -1)
Train the perceptrons¶
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clf1 = Perceptron(eta=0.001, n_iter=1000)
clf2 = Perceptron(eta=0.001, n_iter=1000)
clf3 = Perceptron(eta=0.001, n_iter=1000)
clf4 = Perceptron(eta=0.001, n_iter=1000)
clf = [clf1, clf2, clf3, clf4]
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for i in range(4):
clf[i].fit(X, y[i])
Plot the training session¶
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fig = plt.figure(figsize=(10,10))
for i in range(4):
ax = fig.add_subplot(2,2, i+1)
ax.plot(range(1, len(clf[i].errors_) + 1), clf[i].errors_, marker='.')
ax.set_xlabel('Epochs')
ax.set_ylabel('Misclassifications')
Visualizing the Perceptron¶
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from matplotlib.colors import ListedColormap
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def plot_decision_regions(X, y, classifier, resolution=0.02, ax=plt.figure()):
# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])
# plot the decision surface
x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
Z = Z.reshape(xx1.shape)
ax.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
ax.set_xlim(xx1.min(), xx1.max())
ax.set_ylim(xx2.min(), xx2.max())
# plot class samples
for idx, cl in enumerate(np.unique(y)):
ax.scatter(x=X[np.where(y == cl), 0], y=X[np.where(y == cl), 1],
alpha=0.8, c=cmap(idx),
marker=markers[idx], label=cl)
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fig = plt.figure(figsize=(10,10))
for i in range(4):
ax = fig.add_subplot(2,2, i+1)
plot_decision_regions(X=X, y=y1, classifier=clf[i],ax=ax)
ax.set_xlabel('Domain')
ax.set_ylabel('Agreeableness')
ax.legend(loc='upper left')
Conclusion¶
Hence, we see that a Perceptron fails to classify the data as it is not linearly separable.